In general, financial institutions have automated most check processing systems by printing financial documents, such as account numbers and bank routing numbers onto the checks. Before a check amount is deducted from a payer's account, the amount, account number, and other important information must be extracted from the check. The highly automated form of extraction is done by a check processing control system that captures information from the Magnetic Ink Character Recognition (“MICR”) line. The MICR line consists of specially designed numerals that are printed on the bottom of a check using magnetic ink. The MICR data fields include the bank routing number, bank transit number, account number, check serial number, check amount, process code and extended process code.
Check fraud is one of the largest challenges facing financial institutions today. Advances in counterfeiting technology have made it increasingly easy to create realistic counterfeit checks used to defraud banks and other businesses. Image-based check processing systems play a crucial role in check fraud detection software programs by extracting and verifying various check features that can be found on the check image. In order to be verifiable, an image feature should be either consistent across all check images from the same account, or cross-verifiable against another feature on the same check.
Conventional methods of reducing check fraud include providing watermarks on the checks, fingerprinting non-customers that seek to cash checks, positive pay systems and reverse positive pay systems. Positive pay systems feature methods in which the bank and its customers work together to detect check fraud by identifying items presented for payment that the customers did not issue. With reverse positive pay systems, each bank customer maintains a list of checks issued and informs the bank which checks match its internal information. Although these check fraud security systems have been somewhat effective in deterring check fraud, they suffer from a multiplicity off drawbacks. For example, these systems are generally very slow and prohibitively expensive.
U.S. Pat. No. 5,257,320 discloses a signature verification system wherein, a check is scanned for an actual signature and a corresponding code located on the face of the check. The scanned data is converted into digital form and a software program is used to compare the signature to the code. A pass-fail light is then employed to indicate the result of the comparison. U.S. Pat. No. 5,509,692 teaches a system and method for point of presentation signature verification for a monetary instrument such as a check, wherein the front face of the check comprises a machine-readable representation of an authorized signature. At a point of presentment, the check is scanned and the actual signature on the check is manually or automatically compared with the machine-readable representation of the authorized signature, and a similarity score is generated
One drawback of the above-identified signature verification systems is that they do not involve a compression of account signature data to a fingerprint containing only a small fraction of the account signature data. These references also fail to disclose methods of determining person-specific confidence thresholds by evaluating the complexity and topology of account signatures. In addition, these references provide preprocessing of the signature and extraction features from the signature bitmap rather than applying a signature skeletonization technique, and then extracting features from the signature skeleton. A further drawback of the above-identified systems is that they assume a fixed-size signature representation.
In view of the above drawbacks, there exists a need for a system and method for check fraud detection using signature validation that involves a compression of account signature data to a fingerprint containing only a small fraction of the account signature data.
There further exists a need for a system and method for check fraud detection using signature validation that involves determining person-specific confidence thresholds by evaluating the complexity and topology of account signatures.
It would also be desirable to provide a system and method for check fraud detection using signature validation that involves applying a signature skeletonization technique, and then extracting features from the signature skeleton.
It would further be desirable to provide a need for a system and method for check fraud detection using signature validation does not assume any particular size of the signature.